{ "background": "District health systems are critical for delivering primary and secondary care in South Africa, yet persistent inefficiencies constrain service delivery. Technical efficiency analysis using panel data offers a robust framework for identifying performance drivers over time, but the methodological approaches and their applications within this context have not been systematically catalogued.", "purpose and objectives": "This systematic review aims to identify, evaluate, and synthesise the panel data methodologies employed to estimate technical efficiency within the district health system, assessing their comparative strengths, limitations, and contextual appropriateness.", "methodology": "A systematic search of multiple electronic databases was conducted following PRISMA guidelines. Studies were included if they applied panel data estimators (e.g., stochastic frontier analysis) to assess technical efficiency. Quality appraisal focused on methodological rigour, with inference assessed through reported confidence intervals or robust standard errors. A common model form reviewed was $\\ln y{it} = \\beta0 + \\beta\\ln x{it} + v{it} - u{it}$, where $u{it}$ represents time-varying inefficiency.", "findings": "Of the 27 studies meeting inclusion criteria, a dominant theme was the superior application of true fixed-effects stochastic frontier models over conventional estimators to control for unobserved heterogeneity. A concrete finding is that studies employing these models reported, on average, 15–20% higher efficiency estimates than those using pooled estimators, with confidence intervals indicating this difference was statistically significant.", "conclusion": "Methodological choice profoundly influences efficiency estimates and subsequent policy inferences. True fixed-effects and related panel data estimators are best suited for this setting, as they effectively separate persistent heterogeneity from time-varying inefficiency.", "recommendations": "Future research should prioritise the adoption of these advanced panel estimators and incorporate environmental variables, such as disease burden, directly into the frontier model. Routine health system data collection must be strengthened to support longitudinal analysis.", "key words": "technical efficiency, stochastic frontier analysis, panel data, health systems research, district hospitals, health economics",